Introduction

Welcome to the ‘Get started’ page of the jfa package. jfa is an R package that provides Bayesian and classical statistical methods for audit sampling, data auditing, and algorithm auditing. This page points you to the vignettes accompanying each of these three subjects.

Audit sampling

Firstly, jfa facilitates statistical audit sampling. That is, the package provides functions for planning, performing, and evaluating an audit sample compliant with international standards on auditing (American Institute of Certified Public Accountants (AICPA), 2021; International Auditing and Assurance Standards Board (IAASB), 2018; Public Company Accounting Oversight Board (PCAOB), 2020).

Data auditing

Secondly, jfa facilitates statistical data auditing. That is, the package includes functions for auditing data, such as testing the distribution of first digits of a data set against Benford’s law, or assessing whether a data set includes an unusual amount of repeated values.

Algorithm auditing

Finally, jfa facilitates statistical algorithm auditing. That is, the package implements functions for auditing algorithms, such as computing fairness metrics and testing the equality of parity metrics across protected groups.

References

American Institute of Certified Public Accountants (AICPA). (2021). Clarified statements on auditing standards. American Institute of Certified Public Accountants (AICPA).
International Auditing and Assurance Standards Board (IAASB). (2018). Handbook of international quality control, auditing review, other assurance, and related services pronouncements (vol. i). International Federation of Accountants.
Public Company Accounting Oversight Board (PCAOB). (2020). Auditing standards. Public Company Accounting Oversight Board (PCAOB).